RESEARCH
Earth Systems Lab 2025
We chose our mission statement carefully because it acknowledges our responsibility to the future. A steward nurtures and protects what already exists, while carefully guiding new development in a responsible manner. The principle of ‘optimistic stewardship’ is our guiding star for developing impactful technology and for growing our community.
Our FDL community of brilliant and best minds, along with leading organisations are uniquely placed to show how emerging technologies can be a force for good in the world. Over the last eight years, Earth Systems Lab teams have pushed the boundaries of Earth observation: from world-first demonstrations of machine learning in orbit for mapping floods and landscape changes, to predicting global-scale rainfall, to understanding the drivers of extreme wildfires.
3D CLOUDS FOR CLIMATE EXTREMES
Can we dynamically model 3D cloud structure quantify its importance on extreme climate?
Advancing global 3D cloud reconstruction is needed to improve our understanding of atmospheric phenomena - and climate.
Predictions could support a wide variety of scientific use-cases: from better forecasting of hurricane intensity, to more discriminative cloud classification or a more nuanced understanding of how deforestation affects cloud cover and type.
FOUNDATION MODELS FOR EXTREME ENVIRONMENTS
How can we appropriately use foundation models for uncertainty-aware decision making in poorly sampled environments?
Contemporary Earth Observation foundation models have poor predictive power in environments like the Antarctic - where sparse sampling during training leads to poor generalization (i.e. when the target region is significantly different to training data).
We aim to reliably assess where a model is likely to perform poorly in advance and explore methods to condition or adjust predictions, such as introducing heterogeneous sources of ground truth.
STARCOP 2.0: ATMOSPHERIC ANOMALY DETECTION ONBOARD
Can we push the limits of weak signal detection to map transient phenomena like anthropogenic greenhouse gas leaks?
Constellations of small satellites offer great potential for low-latency Earth Observation tasks like alerting or tip-and-cue.
We will assess the detection limits for weak signals, leveraging rich multi-and hyperspectral data to better identify difficult to detect phenomena like anthropogenic greenhouse gas leaks or other atmospheric anomalies.